Database Storage Engines & Data Engineering Track: Comprehensive Learning & Engineering Roadmap
Unravel how database engines store and retrieve bytes: B+ Trees, Write-Ahead Logs, Cost-Based Optimizers, LSM-Trees in NoSQL, and columnar OLAP analytics.
Architecture & Competency Framework
This engineering track is structured as a sequential dependency graph. Rather than teaching disparate syntax in isolation, each milestone reinforces mathematical foundations, systems invariants, low-level execution semantics, and production debugging patterns. Developers navigating this roadmap progress from memory models and runtime primitives directly into scalable architectural designs and real-world failure handling.
Phase 1: Foundations
Core execution models, compiler toolchains, memory allocations, and baseline theoretical algorithms.
Phase 2: Concurrency & Networks
Asynchronous event loops, socket multiplexing, threading synchronization, and distributed protocols.
Phase 3: Production Scale
Distributed telemetry, fault tolerance, containerization, performance profiling, and systems tuning.
Curriculum Breakdown & Stage-by-Stage Milestones
1. Relational Storage Engines & Page Layout
Learn how databases layout records on NVMe blocks: slotted page architectures, B+ Tree node splitting and rebalancing, buffer pool replacement policies, and page cache flush mechanics.
Key Competencies & Core Topics:
- Slotted-Page Storage Structure & Record Offsets
- B+ Tree Index Anatomy: Leaf Pointers vs Internal Nodes
- Buffer Pool Manager: Clock-Sweep vs 2Q Cache Eviction
- Direct I/O (O_DIRECT) vs Linux Page Cache Double Buffering
2. Transaction Concurrency & Recovery (ARIES)
Guarantee transaction atomicity and durability: Write-Ahead Logging (WAL) write-before-data invariants, ARIES crash recovery algorithm (Analysis, Redo, Undo), and MVCC concurrency.
Key Competencies & Core Topics:
- Write-Ahead Logging (WAL) & Log Sequence Numbers (LSN)
- ARIES Recovery Algorithm: Analysis, Redo, and Undo passes
- Multi-Version Concurrency Control (MVCC) Tuple Visibility
- Two-Phase Locking (2PL) & Rigorous Deadlock Graph Detection
3. Cost-Based Query Optimization & Execution
Dissect how database engines execute queries: relational algebra transformations, System R dynamic programming join ordering, histogram statistics, and vectorized Volcano iterators.
Key Competencies & Core Topics:
- Relational Algebra Query Rewrite Rules & Filter Pushdown
- Cost-Based Optimizer Statistics & Cardinality Estimation
- Join Algorithms: Nested Loop, Sort-Merge Join, and Grace Hash Join
- Volcano Iterator Model vs Block-Vectorized Processing
4. Distributed NoSQL & Columnar Storage
Master modern data store designs: Log-Structured Merge Trees (LSM) in RocksDB, immutable SSTables with Bloom filters, columnar Parquet encoding, and ClickHouse vectorized execution.
Key Competencies & Core Topics:
- Log-Structured Merge (LSM) Trees: MemTable to SSTable flush
- Bloom Filters for Negative Lookup Optimization
- Columnar Storage: Run-Length Encoding (RLE) & Dictionary Compression
- Vectorized SIMD Processing in Columnar Analytics Engines
5. Vector Database Internals & Semantic Search
Index high-dimensional vector embeddings for AI models: Hierarchical Navigable Small World (HNSW) graph indexing, Inverted File with Product Quantization (IVF-PQ), and pgvector execution.
Key Competencies & Core Topics:
- High-Dimensional Cosine, Dot-Product, and L2 Distance Metrics
- Hierarchical Navigable Small World (HNSW) Graph Construction
- Product Quantization (PQ) Compression for 10x RAM Reduction
- Hybrid Search: Combining BM25 Keyword Search with Dense Vector HNSW
Hands-on Engineering Practice & Next Milestones
Completing this roadmap equips you with end-to-end fluency across the stack. Practice by writing production tests, profiling CPU cache misses, tracing network packets with Wireshark/eBPF, and benchmarking system throughput under simulated latency.